American Journal of Materials Science and Engineering
ISSN (Print): 2333-4665 ISSN (Online): 2333-4673 Website: http://www.sciepub.com/journal/ajmse Editor-in-chief: Dr. SRINIVASA VENKATESHAPPA CHIKKOL
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American Journal of Materials Science and Engineering. 2018, 6(1), 18-25
DOI: 10.12691/ajmse-6-1-4
Open AccessArticle

Optimization of Machining Parameter for Surface Roughness in Turning GFRP Composite Using RSM-GA Approach

Md. Rezaul Karim1, Sayem Ahmed1, Samia Salahuddin1 and Md. Rezaul Karim1,

1Department of Mechanical and Production Engineering, Ahsanullah University of Science and Technology, Dhaka, Bangladesh

Pub. Date: May 16, 2018

Cite this paper:
Md. Rezaul Karim, Sayem Ahmed, Samia Salahuddin and Md. Rezaul Karim. Optimization of Machining Parameter for Surface Roughness in Turning GFRP Composite Using RSM-GA Approach. American Journal of Materials Science and Engineering. 2018; 6(1):18-25. doi: 10.12691/ajmse-6-1-4

Abstract

This paper deals with the analysis of surface roughness in turning GFRP composite through experimental investigation and Response surface methodology (RSM) based optimization modeling incorporated with Genetic Algorithm (GA). The investigation has been carried out in dry condition where cutting speed, feed rate and depth of cut has been considered as input parameters to check the desired surface roughness response. This experiment has been designed using RSM central composite design (CCD). Afterwards the response model has been formulated using quadratic RSM model and Genetic algorithm. The correlation coefficient value of 0.9989 suggests the adequacy of the formulated model. Main effect plot and 3D surface plot have been used to evaluate the effect of input parameters followed by Desirability Function Analysis (DFA) through response surface equation of the machining response. Machining parameters were then optimized using GA approach which indicated that to attain advantageous machining response cutting speed and feed rate need to be at 78 m/min and 0.10 mm/rev respectively. These findings are also analogous with the result of DFA which validates both the model. By employing the model, surface roughness of as minimum as 0.056 µm can be achieved.

Keywords:
GFRP composite turning surface roughness response surface methodology desirability function analysis genetic algorithm

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